1. Field of the Invention
The present invention relates to a wireless communication system, and more particularly, to a method of transmitting channel state information in a wireless communication system.
2. Discussion of the Related Art
Recently, MIMO (multiple input multiple output) system is one of the hottest segments in the wideband wireless communication technology. The MIMO system means the system that can raise communication efficiency of data using multiple antennas. And, the MIMO systems can be categorized into space multiplexing scheme and space diversity scheme according to a presence or non-presence of the same data transmission.
The space multiplexing scheme means the scheme for transmitting data at high speed by transmitting different data via a plurality of transmitting antennas simultaneously without increasing a bandwidth. The space diversity scheme means the scheme for obtaining transmission diversity by transmitting the same data via a plurality of transmitting antennas. Space time channel coding is an example for the space diversity scheme.
The MIMO technique can be also categorized into an open loop scheme and a closed loop scheme according to a presence or non-presence of feedback of channel information to a transmitting side from a receiving side. The open loop scheme includes a space-time trellis code (STTC) scheme for obtaining a transmission diversity and coding gain using BLAST and space region capable of extending an information size amounting to the number of transmitting antennas in a manner that a transmitting side transmits information in parallel and that a receiving side detects a signal using ZF (zero forcing) and MMSE (minimum mean square error) scheme repeatedly. And, the closed loop scheme includes a TxAA transmit antenna array) scheme or the like.
In the following description, spatial channel matrix usable for the present invention shall be schematically explained.
      H    ⁡          (              i        ,        k            )        =      [                                                      h                              1                ,                1                                      ⁡                          (                              i                ,                k                            )                                                                          h                              1                ,                2                                      ⁡                          (                              i                ,                k                            )                                                …                                                    h                              1                ,                Nt                                      ⁡                          (                              i                ,                k                            )                                                                                      h                              2                ,                1                                      ⁡                          (                              i                ,                k                            )                                                                          h                              2                ,                2                                      ⁡                          (                              i                ,                k                            )                                                …                                                    h                              2                ,                Nt                                      ⁡                          (                              i                ,                k                            )                                                            ⋮                          ⋮                          ⋱                          ⋮                                                                h                              Nr                ,                1                                      ⁡                          (                              i                ,                k                            )                                                                          h                              Nr                ,                2                                      ⁡                          (                              i                ,                k                            )                                                …                                                    h                              Nr                ,                Nt                                      ⁡                          (                              i                ,                k                            )                                            ]  
In this matrix, the H(i,k) is a spatial channel matrix, the Nr indicates the number of receiving antennas, the Nt indicates the number of transmitting antennas, the r indicates an index of a receiving antenna, the t indicates an index of a transmitting antenna, the i indicates an index of an OFDM or SC-FDMA symbol, and the k indicates an index of a subcarrier.
The hr,t(i,k) is an element of the channel matrix H(i,k) and means an rth channel state and a tth antenna on an ith symbol and a kth subcarrier.
Spatial channel covariance matrix usable for the present invention is schematically explained as follows. The spatial channel covariance matrix can be represented as a symbol R.R=E[Hi,kHi,kH]
In this matrix, the H indicates a spatial channel matrix and the R indicates a spatial channel covariance matrix. The E[ ] means an average, the i indicates a symbol index, and the k indicates a frequency index.
Singular value decomposition (SVD) is one of major methods for decomposing a rectangular matrix and is the scheme frequently used in the fields of signal processing and statistics. The SVD is generated from generalizing the spectrum theory of matrix for an arbitrary rectangular matrix. In case of using the spectrum theory, it is able to decompose an orthogonal square matrix into diagonal matrixes on the base of an eigen value. Assume that a matrix H is an m×m matrix consisting of elements of a set of real or complex numbers. In this case, the matrix H can be represented as multiplications of 3 matrixes shown in the following.Hm×m=Um×mΣm×nVn×nH 
In this case, the U and V indicate unitary matrixes, respectively. The Σ indicates m×n diagonal matrix including a singular value that is not negative. The singular value is represented as Σ=diag(σ1 . . . σr), σi=√{square root over (λi)}. Thus, the representation of the multiplication of three matrixes is called singular value decomposition. The singular value decomposition can handle more general matrixes rather than the eigen value decomposition capable of decomposing an orthogonal square matrix only. And, the singular value decomposition and the eigen value decomposition are related to each other.
When a matrix H is a positive definite Hermitian matrix, all eigen values of the H are non-negative real numbers. In this case, a singular value and vector of the H become equal to an eigen value and vector of the H, respectively.
Meanwhile, the eigen value decomposition (EVD) can be represented as follows.HHH=(UΣVH)(UΣVH)H=UΣΣTUH HHH=(UΣVH)(UΣVH)H=VΣTΣV 
In this case, the eigen value can be set to λ1, . . . , λr.